1. Field of the Invention
The present invention relates to systems and methods for assessing the health conditions of individuals. In particular, the present invention relates to systems and methods for using wavelet transform to analyze heart diagnostic signals of individuals and to compare the analyzed results to a reference database of similarly wavelet transformed and analyzed signals to assess the cardiovascular health of the individuals and to control how the heart diagnostic signals are generated.
2. Description of Related Art
Cardiovascular disease is a serious health problem and a leading cause of death around the world. The effective treatment of cardiovascular disease depends on the early detection and diagnosis of heart abnormalities. One common tool for detecting and diagnosing cardiovascular conditions is the electrocardiogram (ECG) machine. An ECG machine uses probes that are attached to various points on a patient's limbs and chest to measure changes in electrical signals (ECG signals) generated by the patient's heartbeat. A physician may visually analyze the ECG signals to assess the health of the heart or to identify signature waveforms that may correspond to a heart disorder. One drawback of visual analyses is that subtle details in the waveforms may not be readily observable to the naked eyes, causing a mis-identification of the underlying cardiovascular condition. To improve the accuracy of diagnosis, signal processing of the ECG signals may be used to extract the finer details of the waveforms.
One such signal processing technique is using Fourier transform to transform ECG signals from the time domain to the frequency domain to extract the frequency domain information of the ECG waveforms. Such frequency domain information may include the distribution of signal energy across the frequency bands, the spectral characteristics of the frequency bands, the bandwidth of the signal energy, etc. A premise of Fourier transform analysis is that the time domain signal operated by the Fourier transform is stationary—that is, the spectral characteristics of the signal do not change with time. However, ECG signals are inherently non-stationary stochastic signals. One way to overcome the limitation of the Fourier transform is to treat an ECG signal as the superposition of many short signal segments, to run a Fourier transform on each signal segment separately, and to combine the Fourier transforms of the signal segments to construct the spectral signature of the overall signal. However, such technique is computationally intensive and thus impractical to implement.
Wavelet transform is a signal processing technique that generates information in both frequency and time domains, and is increasingly being used to process ECG signals. Wavelet transform may operate on non-stationary waveforms by using a series of scaled and translated localized oscillating base functions to orthogonally project the waveforms to a frequency domain of variable frequency resolutions. Wavelet transform may automatically adapt to the non-stationary nature of the waveforms to achieve a good balance of time-frequency resolutions. For example, a fast changing waveform may be sampled at a higher rate to achieve higher time resolution but lower frequency resolution, while a slow changing waveform may be sampled at a slower rate to achieve higher frequency resolution but lower time resolution.
While wavelet transform has been adapted to process ECG signals, it has not been fully exploited to help health professionals identify underlying physical and pathological cardiovascular conditions of the patients whose ECG signals are analyzed. In addition, results of the wavelet transform analysis have not been effectively used to configure the ECG devices to optimally capture the ECG signals of the patients. As a result, correctly diagnosing the conditions of the patients has been challenging. As such, it is desirable to have systems and methods that better use wavelet transform to analyze ECG signals to more accurately and more robustly identify cardiovascular conditions of patients. It is also desirable to use the results of the analysis to configure the ECG devices to better capture the ECG signals.